We have developed an automatic abstract generation system for Japanese expository writings based on rhetorical structure extraction. The system first extracts the rhetorical structure, the compound of the rhetorical relations between sentences, and then cuts out less important parts in the extracted structure to generate an abstract of the desired length. Evaluation of the generated abstract showed that it contains at maximum 74% of the most important sentences of the original text. The system is now utilized as a text browser for a prototypical interactive document retrieval system.

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In this paper we report results of an investigation into EnglishJapanese Cross-Language Information Retrieval (CLIR) comparing a number of query translation methods. Results from experiments using the standard BMIR-J2 Japanese collection suggest that full machine translation (MT) can outperform popular dictionary-based query translation methods and further that in this context MT is largely robust to queries with little linguistic structure.

Local feedback for ad hoc retrieval typically hurts performance for about one-third of the search requests while improving the average performance. Our objective is to make it more reliable by estimating the optimal number of assumed-relevant documents and the optimal number of expansion terms for each request. We examine some simple optimization methods based on: the number of case particles in the request; the number of initial search terms; the highest document score in the initial ranked output; and the document score curves. Unfortunately, our first results using the BMIR-J2 and IREX Japanese test collections are negative. We are currently exploring some modified strategies for solving the optimization problems.